A Kalman filter approach based on random drift data of Fiber Optic Gyro

Fiber Optic Gyro (FOG) based on the Sagnac effect is presently used in the Strapdown Inertial Navigation System (SINS) for its outstanding merits. However, the sensor errors of FOG still affect the accuracy of the whole system greatly. The gyro sensor errors consist of two parts: a deterministic part and a random part. The random part is basically due to the gyro random drift and primarily includes the measurement noise. Kalman filter (KF) is applied to on-the-fly filtering the noisy drift data of FOG and reduce its random noise. This paper suggests improving the gyro random error model for the KF by wavelet multiple level of decomposition before filtering the gyro drift data. The improved KF approach was applied to a tactical grade FOG and the results showed that the random noises in gyro drift data could be greatly reduced, e.g. bias instability noise has turned from 1.85 deg/hr to 0.16 deg/hr.